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Application domain extension of incremental capacity-based battery SoH indicators

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  • Ospina Agudelo, Brian
  • Zamboni, Walter
  • Monmasson, Eric

Abstract

The Incremental Capacity (IC) analysis is used to characterise the capacity and the battery state of health, aged by cycling patterns with randomly selected pulsed current levels and duration. The batteries are periodically characterised at 1C current, which is a high value with respect to the typical IC tests in pseudo-equilibrium condition. The high-current IC curves generation from raw voltage/current data includes two filtering stages, one for the input voltage and one for the incremental capacity curve smoothing, which are optimised for the application on the basis of the data characteristics. The correlations between the IC main peak features and the battery full capacity for 28 Lithium–Cobalt oxide batteries with 18650 packaging were evaluated, finding that the main peak area is a general feature to evaluate the state of health under high current tests and random usage pattern, and, therefore, it can be used as a battery health indicator in practical applications. The effects of the computational parameters on the relationship between the peak area and the battery capacity are also investigated. The results are confirmed by a further analysis performed over an additional set of cells with different technology, aged with a fixed cycling pattern. Additionally, the performance of the peak area as a health indicator was compared with an ohmic resistance-based estimation approach.

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  • Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:energy:v:234:y:2021:i:c:s0360544221014729
    DOI: 10.1016/j.energy.2021.121224
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    6. Li, Renzheng & Hong, Jichao & Zhang, Huaqin & Chen, Xinbo, 2022. "Data-driven battery state of health estimation based on interval capacity for real-world electric vehicles," Energy, Elsevier, vol. 257(C).
    7. Zhang, Qisong & Yang, Lin & Guo, Wenchao & Qiang, Jiaxi & Peng, Cheng & Li, Qinyi & Deng, Zhongwei, 2022. "A deep learning method for lithium-ion battery remaining useful life prediction based on sparse segment data via cloud computing system," Energy, Elsevier, vol. 241(C).
    8. Ji, Jie & Zhou, Mengxiong & Guo, Renwei & Tang, Jiankang & Su, Jiaoyue & Huang, Hui & Sun, Na & Nazir, Muhammad Shahzad & Wang, Yaodong, 2023. "A electric power optimal scheduling study of hybrid energy storage system integrated load prediction technology considering ageing mechanism," Renewable Energy, Elsevier, vol. 215(C).
    9. Li, Xiaoyu & Lyu, Mohan & Li, Kuo & Gao, Xiao & Liu, Caixia & Zhang, Zhaosheng, 2023. "Lithium-ion battery state of health estimation based on multi-source health indicators extraction and sparse Bayesian learning," Energy, Elsevier, vol. 282(C).
    10. Brian Ospina Agudelo & Walter Zamboni & Eric Monmasson, 2021. "A Comparison of Time-Domain Implementation Methods for Fractional-Order Battery Impedance Models," Energies, MDPI, vol. 14(15), pages 1-23, July.
    11. Zhang, Zhengjie & Cao, Rui & Zheng, Yifan & Zhang, Lisheng & Guang, Haoran & Liu, Xinhua & Gao, Xinlei & Yang, Shichun, 2024. "Online state of health estimation for lithium-ion batteries based on gene expression programming," Energy, Elsevier, vol. 294(C).
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    13. Zhaosheng Zhang & Shuo Wang & Ni Lin & Zhenpo Wang & Peng Liu, 2023. "State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles Based on Regional Capacity and LGBM," Sustainability, MDPI, vol. 15(3), pages 1-20, January.
    14. Li, Xining & Ju, Lingling & Geng, Guangchao & Jiang, Quanyuan, 2023. "Data-driven state-of-health estimation for lithium-ion battery based on aging features," Energy, Elsevier, vol. 274(C).
    15. Jiang, Nanhua & Zhang, Jiawei & Jiang, Weiran & Ren, Yao & Lin, Jing & Khoo, Edwin & Song, Ziyou, 2024. "Driving behavior-guided battery health monitoring for electric vehicles using extreme learning machine," Applied Energy, Elsevier, vol. 364(C).
    16. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).

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